Understanding Machine Learning Algorithms In Python

Posted By : Anirudh Bhardwaj | 25-Feb-2020

Machine learning algorithms in Python app development

Machine learning algorithms play a critical role in delivering engaging user experiences by accurately analyzing user behavior through historical data. As of today, an increasing number of enterprises rely on ML-based applications to monitor their customer behavior and identify consumer preferences. The machine learning data enables them to formulate effective trade strategies, in-line with the preferences and expectations of their potential customers. That being said, the demand for machine learning app development is rapidly increasing to fulfill the dynamically changing enterprise requirements. Nevertheless, a majority of businesses seek Python app development services for implementing machine learning algorithms. 


At Oodles Technologies, we have gained extensive experience in Python-based machine learning application development. Our development team is skilled at using the latest Python frameworks and agile methodologies to develop high-performance machine learning applications. Based on our past experience in Python app development, we have listed down the five most effective ML algorithms in Python.

Also read Using AI with AR and VR To Enhance Customer Experience


Popular Machine Learning Algorithms In Python

Below are the most popular machine learning algorithms used in Python. 


Linear Regression

It is the most fundamental and yet the most significant machine learning algorithm that every data scientist must know. Linear regression lays the groundwork for all the complex ML algorithms and thus, is the most significant of all. In technical terms, linear regression is a statistical technique used to establish relationship between a dependent variable and a set of independent variables. In Python, linear regression is mainly divided into two categories:


  • Simple Linear Regression

Also known as univariate linear regression, this technique is used for predicting responses using a single variable or feature. In Python-based single linear programming, we consider a linear relationship between two variables. The algorithm aims at discovering a linear function that provides the most accurate response to the independent linear variable.


  • Multiple Linear Regression

Multiple linear regression technique aims at modelling the relationship between the response function and two or more variables in accordance with a linear equation. It is simply an extension of a single linear regression algorithm.


Both single and multiple linear regression algorithms are specifically useful in implementing machine learning models that incorporate supervised learning. For example, you can train a computer to automatically filter spam emails based on the latest information. Furthermore, these algorithms are also used in consumer behavior forecasting in Python-based machine learning application development.


Logistic Regression

It is a supervised learning-based ML-algorithm in Python that aims at identifying discrete values like 0 and 1. In this way, it enables computers or machines to estimate simple functions like Yes/No and True/False. The algorithm uses a given set of independent variables and a logistic function to predict the possibility of an event. The output for every event comes in the binary values, 0 and 1. 


This type of algorithm is widely used for the classification of problems based on two outputs i.e yes/no or true/false values. For example, developers can implement this algorithm to identify if the given email is spam or not. Similarly, when fed with the medical history of a patient, this algorithm can be used to predict the possible illness with a fatal disease. 


Decision Tree

A decision tree is yet another example of supervised machine learning algorithm. It enables computers to solve problems related to both regression and classification. However, it is majorly used for classification and comparison of objects, images, and numerous other entities. When implemented in Python, the algorithm compares the key features associated with the given entity as per a predefined conditional statement. For every instance, it traverses a tree, compares the features, and provides the outcome for an effective decision making.


There are mainly two types of decision trees:


  • Classification Trees

A classification tree is a simple Yes/No type of tree in which the decision variable is categorical. 


  • Regression Trees

Regression trees incorporate continuous data type i.e here, the decision variable is continuous.


Naive Bayes

Also known as Naive Bayes Classifier, it is a classification-based algorithm that works on the assumption of independent predictors. When implemented in Python app development, Naive Bayes algorithm assumes that a variable is independent of the other variables or features. As the name suggests, the algorithm is based on a popular mathematical computation called Bayes Theorem. 


For example, the algorithm can accurately predict whether a ball is a football or not based on the given features and characteristics. Similarly, the algorithm can be used for the classification of countless objects or entities with high accuracy. 


The algorithm is easy to implement in Python and is effective at handling large and complex data sets. 


Random Forest

It is a complex version of the decision tree algorithm which combines several decision trees to perform random sampling of data sets. In random forest, there are multiple trees and each tree is trained with a random subset of data. It makes the algorithm unbiased and more accurate than decision tree algorithm. In addition, random tree is a more stable algorithm as it remains unaffected by the dynamic changes in the given dataset. Nevertheless, the algorithm works perfectly well for both numerical and categorical variables or features.  


You may also be interested in reading An Introduction To Logger in Python


Closing Thoughts

In this blog post, we have discussed the five most extensively used machine learning algorithms in Python application development. Every aspiring data scientist or Python developer must be familiar with these algorithms to make a career in machine learning app development. There are several other significant algorithms that are increasingly popular in Python app development processes including k-Nearest Neighbors, k-Means, and, support vector machines.   


Why Choose Oodles For ML-based Python Application Development?

We are a 360-degree Python app development company that builds cross-platform web and mobile applications using Python-based programming. We use the latest Python development frameworks including Django and Flask for delivering high-performance enterprise app solutions. Our development team is skilled at implementing machine learning algorithms in Python-based application development projects.

About Author

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Anirudh Bhardwaj

Anirudh is a Content Strategist and Marketing Specialist who possess strong analytical skills and problem solving capabilities to tackle complex project tasks. Having considerable experience in the technology industry, he produces and proofreads insightful content on next-gen technologies like AI, blockchain, ERP, big data, IoT, and immersive AR/VR technologies. In addition to formulating content strategies for successful project execution, he has got ample experience in handling WordPress/PHP-based projects (delivering from scratch with UI/UX design, content, SEO, and quality assurance). Anirudh is proficient at using popular website tools like GTmetrix, Pagespeed Insights, ahrefs, GA3/GA4, Google Search Console, ChatGPT, Jira, Trello, Postman (API testing), and many more. Talking about the professional experience, he has worked on a range of projects including Wethio Blockchain, BlocEdu, NowCast, IT Savanna, Canine Concepts UK, and more.

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